This review describes the requirements and applications of bioelectrical impedance vector analysis in clinical practice in adult patients.
Review Article
This review describes the requirements and applications of bioelectrical impedance vector analysis in clinical practice in adult patients.
Body composition assessment is crucial in clinical nutrition practice for screening, diagnosis, treatment, and prognosis of patients. Bioelectrical impedance analysis (BIA) is widely used to assess body composition. Impedance devices measure the body's electrical response (resistance) when exposed to an electrical current, thereby indirectly estimating body composition by conducting a low electrical current into the body. However, multiple factors, including devices, equations, indices, and population references, affect the interpretation of BIA results. Bioelectrical impedance vector analysis (BIVA) has been developed as an alternative method for semi-quantitative assessment of hydration status, which overcomes some limitations of traditional BIA by analyzing vector distance patterns and their distribution on resistance and reactance (R-Xc). This review describes the BIA device classification, which is useful for determining when the BIVA approach is appropriate for body composition analysis. This narrative review outlines the requirements and medical and nutritional uses of bioelectrical impedance vector analysis in adult patients over the past decade. Several publications have reported the clinical applications of BIVA in adult patients, including the evaluation of fluid volume status, control of body fluids, and prediction of negative outcomes in patients undergoing renal replacement therapy or admitted to critical care units. It has also been used as an alternative diagnostic tool in adults with fluid abnormalities to detect sarcopenia, cachexia, or malnutrition, reflecting low body cell mass or a decrease in soft tissue. However, some implications must be considered to avoid affecting the interpretation of the results, including differences in devices, analysis methods, and specific population references. Thus, this review aims to describe the differences between the devices and analysis techniques used for BIVA, which will be discussed in this review.
Assessment of body composition is one of the primary elements to consider regarding nutritional status. Altered body composition occurs when nutrient absorption and/or intake, both deprivation and overconsumption, are affected and is linked to several medical conditions, such as cardiovascular, endocrinological, and bone diseases1,2. Thus, its evaluation can provide crucial information during the screening, diagnosis, treatment, and prognosis of a patient2. Bioelectrical impedance analysis (BIA) is one of the most widely reported methods for assessing body composition in both clinical and research settings because of its noninvasive, low-cost, portable, and reliable nature3,4.
The first application of electrical currents for body composition estimation from Ohm's law was performed by Nyboer et al. in 1950 and Thomasset in 1962 for biological tissues as electrical conductors1. Body compartment analysis using BIA assumes the human body as a cylinder formed by conductive materials. Following Ohm's law, volume can be estimated when a controlled current is induced at a specific frequency, considering length (L) and cross-sectional area (A) of a conductor2,3. Thus, R=ρL/A, where R is the resistance in Ohms, ρ is the resistivity of the conducting material, and LA equals volume (V). The formula transformation is V= ρL2/R3,4. Biological tissues exhibit two types of resistance (R): conductive resistance (lean mass and body fluids) and capacitative R or (reactance, Xc) from insulators (bone, fat mass, and cellular membranes)2,4. The current flows through the extracellular fluids and muscle fibers with relatively low resistance, whereas the capacity to penetrate the cellular membrane depends on the frequency applied. Standard BIA uses a 50 kHz frequency, where the direct current reflects the R value, and the alternating current reflects the delay on the current flow due to the capacitative property of the cellular membrane (Xc); the capacitance is defined as the capacity to store temporally an electrical charge, which causes the delay on the flow4,5,6. The relationship between R and Xc is the impedance (Z), formulated as Z2 = R2+Xc2, reflecting intracellular fluid and cellular components6. Phase angle (PhA) describes Z position as PhA (degree) = [arc tangent (Xc/R)] × (180°/π)5,6. The raw parameters measured by the BIA devices are R, Xc, and PhA (Figure 1). These raw parameters are used in conjunction with anthropometric parameters, such as height (corresponding to the conductor length in the cylinder model), weight, age, and sex, to obtain different components of human body composition2,6. BIA can be better understood by classifying it based on frequency, body region, and data analysis, as described below7.
By frequency, devices could be classified as 1) Single-frequency devices use a frequency of 50 kHz and remain the most common worldwide. The advantages of this method include the availability of raw data for analysis, low cost, and easy calibration2. 2) Multifrequencydevices operate at 2-6 frequencies (usually 1, 5, 50, 100, 200, and 500 kHz); alternating current administered at frequencies ≥5kHz penetrates cell membranes to some degree8; however, frequencies >50 kHz are used to model the resistance at infinite frequency to predict TBW. This enables TBW calculation and ECW extraction, resulting in ICW and fluid distribution9,10. Some multi-frequency devices do not provide raw data9. Moreover, 3) spectroscopy devices use the polynomial Cole-Cole model, which extrapolates R0 to R∞according to measures of Z and PhA at multiple frequencies to generate a Cole plot6,9. The obtained electrical parameters were used to estimate body composition volumes using equations based on tissue electrical properties, according to the Hanai model9. However, the assumptions made by the model do not apply to patients with altered adipose tissue distribution and fluid imbalance8. Studies on this technology are increasing, but many do not specify that they are spectroscopy-based, limiting comparisons2. Access to raw data must be considered because not all spectroscopy devices report R and Xc at a specific frequency, restricting the possibility of employing other data analysis methods6,10. Clinical research has not yet demonstrated the clear advantages of spectroscopy over multiple-frequency devices6.
Body region analysis, divided by whole body, considers the human body as a symmetrical cylinder with a homogeneous composition, generating volume estimation using population equations6,9. Measurement is performed with the standard tetrapolar arrangement, which involves the placement of two electrodes on the hand (one on the wrist between the styloid processes of the ulna and radius and the other just behind the metacarpals) and two electrodes on the foot (one on the ankle between the medial and lateral malleoli and the other just behind the metatarsals)9 also named hand-to-foot on one side, making it practical for clinical use in various positions, with bandaged limbs, or with venoclysis2. Furthermore, segmental measurement, which considers the human body as five cylinders (arms, legs, and trunk) with different resistivities. It requires both feet and arms for tetrapolar or octapolar measurements, and the lack of standardization in electrode placement increases the risk of measurement error2,4. Reference data are limited, and the sum of the segmental impedances differs from the whole-body impedance2.
Data analysis can be conducted using raw parameters such as phase angle, which represents the geometric relation of resistance to reactance. It is an indicator of the distribution of body water and integrity of the cell membrane and has been reported as a prognostic marker for survival, complications, hydration, nutrition, and muscle quality2. Regression equations allow quantitative estimation of body tissue components3. These equations use R as the main predictor, along with height, Xc, weight, and age3. Common parameters include fat-free mass, appendicular skeletal muscle mass, fat mass, body cell mass, and total, extracellular, and intracellular water2. The equations assume equal distribution of conductive properties, constant cross-sectional area, and proportional conductor length, allowing volume calculation when the current flow is controlled2,3. The results are affected by the hydration state and muscle mass changes8. While most equations correlate well with other assessment methods in healthy subjects, they do not correlate well in patients with malnutrition, dehydration, or overhydration, such as those hospitalized with renal, liver, and heart failure. In addition, device differences and age-, sex-, and ethnicity-specific equations can cause inconsistencies in the results2,12. Alternatively, bioelectrical impedance vectorial analysis (BIVA), introduced by Piccoli et al. in 1994 as an alternative to regression equations, uses raw BIA measurements (R and Xc at 50 kHz) divided by height in meters. The impedance vector of a normal person usually falls within the 75th tolerance ellipse on graphs based on ethnicity and sex13,14,15.
This narrative review outlines the requirements and medical and nutritional uses of bioelectrical impedance vector analysis in adult patients over the past decade.
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Requirements to perform bioelectrical impedance vector analysis and interpretation
Before discussing the clinical application of bioelectrical impedance vector analysis (BIVA), it is crucial to recognize that not all commercially available devices are suitable for performing BIVA. Figure 2 presents an algorithm that should be consulted to ensure that the raw bioelectrical impedance required for analysis is accessible and that the device meets the necessary criteria for its use. Additionally, it is essential to verify the reliability, precision, and accuracy of the device through standardization and quality control of raw parameter acquisition, ensuring a trustworthy interpretation across various methods and enhancing the quality of the results7. Quality control includes using the same device with regular device calibration using a manufacturer-supplied test circuit with known resistance or impedance, high-quality electrodes and their position, skin and core temperature, adequate subject preparation, and consistent measurement protocols as measured in the same body and limb positions. González-Correa et al. proposed a protocol as a checklist to standardize BIA measurements in adults16, and how to perform this protocol has been described previously17.
BIVA determines fluid overload or dehydration status according to the vector position in the RXc graph along the vertical axis of R and Xc. Vectors falling below or above the 75th tolerance ellipse in the lower pole indicate fluid overload (extracellular volume expansion) and dehydration, respectively. All vectors falling within the 50th and 75th tolerance ellipses specified normal fluid and cell mass status6,13,15 (Figure 3). Examples of clinical applications are described in Figure 4.
Clinical applications of bioelectrical impedance vectorial analysis
The clinical practice applications reported in PubMed from 2015 to 2025 are outlined in Table 1 and include the use of BIVA to assess fluid hydration status (detecting dehydration, overhydration, fluid accumulation, and fluid redistribution) and nutritional status (malnutrition, cachexia, sarcopenia, muscle mass, and fat mass). Studies involving athletes were excluded. The filters applied included human species, adult populations, and English.
Fluid volume status assessment
BIVA has been described to assess fluid volume status, control body fluids, manage intravenous fluid administration, and detect or predict negative outcomes. One of the first clinical applications of this graph method was in hemodialysis patients to monitor hydration as an alternative to changes in weight pre- and post-dialysis18. Whereas Samoni et al.19described its use in critically ill patients in the critical care unit, reporting that severe hyperhydration evaluated by BIVA was associated with mortality. Kammar-García et al.20reported that patients admitted to the Emergency Department who were classified as having fluid overload by BIVA had an increased probability of mortality. Pineda-Juarez found that ischemic disease patients with altered hydration status according to BIVA presented a lower cardiopulmonary response21. Varaldo et al.22found that acromegalic patients with active or controlled diseases presented an overhydration state out of the 75th percentile for the healthy population.
Another application is to evaluate fluid changes and drive fluid therapy. Costa et al. reported a progressive increase in fluid accumulation with a significantly decreased vector length 24h after cardiac surgery23. Meanwhile, Maioli et al. found that the evaluation of BIVA at admission in patients with stable coronary artery disease allows adjustment of intravascular volume expansion, resulting in lower CI-AKI occurrence after angiographic procedures24.
Group comparisons by BIVA confidence
On the other hand, if the objective is to compare groups of patients, the mean, standard deviation, and correlation of R/H and Xc/H can be registered in BIVA confidence software, which allows for determining significant differences through Hotelling's T2-test and Mahalanobis' generalized distance25. The vector distribution of the group mean values was graphed with respect to the 95th percentile, referred to as the confidence ellipse or confidence interval26. BIVA confidence comparison has been reported in post-myocardial infarction patients with a Charlson comorbidity index of ≥3 vs. <2. Those with a higher index have longer vectors shifted to the right, suggesting a loss of soft tissue27. Guerrini et al. found significant differences in the vector distribution ellipses between patients with sarcopenia or malnutrition, characterized by a large rightward shift of their impedance vectors compared to patients without malnutrition or sarcopenia, indicating a lower soft tissue mass. BIVA also identified subjects with greater and lesser improvements after rehabilitation in subacute post-stroke patients, showing a leftward shift of the admission vectors, which could be interpreted as an increase in soft tissue and muscle mass28.
Bioelectrical impedance vector analysis z-score
BIVA software includes the BIVA Z-score analysis, which is calculated based on the number of standard deviations away from resistance/height and reactance/height values of a subject from the means of the reference population, converting them into a bivariate Z-score, and plotting them on the R-Xc Z-score graph6,29. If the subject vector is in the fourth quadrant (lower-right) and outside the 75th percentile tolerance ellipses, that is, -1 Xc-Z score indicates extracellular expansion and soft tissue waste30. The use of z-scores has been explored little in the literature; it has been reported for determining differences in body composition according to the type and stage of cancer31, evaluating the nutritional status in patients with decompensated cirrhosis32, and in subacute post-stroke patients29.
Body cell mass, cachexia, and sarcopenia assessment
In addition, it can be useful for the classification of cachexia, determined by high resistance and low reactance values, represented by a vector to the right of the graph, >75th of the tolerance ellipse, indicating a lower body cell mass, as in the study by Santillán-Díaz et al., where this approach was used to evaluate the prevalence of rheumatoid cachexia in patients with rheumatoid arthritis33. They found that patients with cachexia presented lower handgrip strength and physical function and higher methotrexate prescription rates. Ramos-Vázquez et al. found a higher frequency of cachexia in patients with oropharyngeal dysphagia who were exclusively tube-fed than in those with oral intake34.
Finally, the main advantages of this BIVA approach are the simultaneous analysis of hydration and body cell mass components and the capacity for proportionate continuous analysis of health status, as continuous measurements can be plotted on the same graph to compare body composition modification (both hydration and nutritional status). The paired one-sample Hotelling's T2 test and Mahalanobis' generalized distance can be used to evaluate the fluid and changes in the dR/H-dXc/H graph30. Lozada-Mellado et al. evaluated changes in hydration status and body cell mass after a dynamic exercise program in patients with rheumatoid arthritis in Mexico, finding a displacement into the cachexia mass quadrant in the patients who did not participate in the exercise program35. Quizzini et al. applied this analysis to older adults with sarcopenia to evaluate the effect of a resistance exercise program without improvement36.
As shown in Table 1, several studies have reported BIVA analysis; however, only BIA parameters were used, such as muscle mass (kg), ECW, TBW, and PhA19,20,21,22,23,24,28,32,33,34,35,36,37,38,39,40,41. Vectorial classification or displacement of the vector according to tolerance ellipses or R-Xc axes were not used or reported37,38,39,40,41.
Perspectives
Future research could evaluate whether patients with abnormalities in body fluids, back to the 75th reference, that is, normal fluid status, have a better prognosis or a lower incidence of worse outcomes, as well as quantify this change in Z score.
In patients with fluid overload or fluid imbalance, muscle mass values are biased because of the physiological relationship with soft tissue hydration30, overestimating muscle mass values and hindering the detection of sarcopenic obesity. To detect the presence of fluid alterations and determine whether regression equations can be applied, the BIVA Z-score could be useful in evaluating fluid status in discriminating against patients with overhydration who could be misclassified without sarcopenia or cachexia. Therefore, new research focusing on this method needs to be conducted.
The evaluation of the effect of medical nutritional therapy to differentiate between fluid accumulation or an increase in body cell mass, which can be combined with a functional capacity test, such as handgrip strength, is another clinical application for further research. The main challenges include the lack of standardization in the application of the method, the need to distinguish between statistically significant differences and those with clinical relevance, and the misunderstanding or unwillingness to accept the limitations and scope of this technique. These aspects are critical when bioimpedance is used as an evaluation tool7. It is important to consider that raw BIA measurements differ between devices and may affect BIVA results, which must be considered when comparing studies and selecting a reference population, as this can impact the interpretation of results, particularly when compared to other reference populations from different devices42. Additionally, the classification categories could be problematic in certain populations, such as women older than 60 years, as there could be differences due to the devices or other factors that warrant interpretation43.
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Bioelectrical impedance analysis (BIA) provides several raw parameters, such as resistance, reactance, and phase angle, which can be visualized and interpreted using BIVA. These components are graphically represented through RXc-graphs, where the position and displacement of the impedance vector within the tolerance ellipses offer insights into the hydration status and body cell mass. However, most studies to date have focused on BIA-derived estimates rather than fully utilizing vectorial classification or reporting vect...
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The authors declare no conflicts of interest.
The authors would like to thank Prof(s). Piccoli and Pastori of the Department of Medical and Surgical Sciences, University of Padova, Italy, for providing the BIVA software.
This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. This protocol/research is part of the Ph.D. dissertation of Yunuen Reyes-Paz, supported by a National Council of Science and Technology (CONACYT) scholarship (CVU 1076076).
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